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Together AI

  • What it is:Together AI is a research-driven AI company that provides a cloud platform for developers and researchers to train, fine-tune, and deploy generative AI models while contributing leading open-source research, models, and datasets.
  • Best for:AI developers needing model variety, Cost-conscious enterprises, Teams migrating from OpenAI
  • Pricing:Free tier available, paid plans from Usage-based with discounts
  • Rating:88/100Very Good
  • Expert's conclusion:Technical teams that prioritize performance and cost efficiency while using open source AI models at scale will find Together AI a great fit.
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Reviewed byMaxim Manylov·Web3 Engineer & Serial Founder

What Is Together AI and What Does It Do?

Together AI is a research-oriented company that provides a native AI platform for training, fine-tuning, deployment, and scale of generative AI models. In addition to offering developer, researcher and enterprise customers, the company also has an ecosystem of hardware and software tools including GPU Clusters, Inference Engines and Open Source Tools.

Active
📍San Francisco, CA
📅Founded 2022
🏢Private
TARGET SEGMENTS
DevelopersAI ResearchersEnterprisesAI StartupsUniversities

What Are Together AI's Key Business Metrics?

👥
45K+
Registered Users
📊
$228.5M
Total Funding
📊
$1.25B
Valuation
📊
10+ global locations
Offices
👥
Fortune 500, universities, AI startups
Customers
📊
3x month-over-month
Traffic Growth

How Credible and Trustworthy Is Together AI?

88/100
Excellent

An AI infrastructure company backed by Top Venture Capitalists and with Proven Technical Execution with Rapid Enterprise Adoption for a Company less than two years old.

BREAKDOWN
Product Maturity75/100
Company Stability92/100
Security & Compliance85/100
User Reviews80/100
Transparency88/100
Support Quality82/100
TRUST SIGNALS
Backed by NVIDIA, Kleiner Perkins, Salesforce Ventures$1.25B valuation in 20 months45K+ registered developersUsed by Stanford, Carnegie Mellon, Fortune 500Global data center presence (US/EU)

What is the history of Together AI and its key milestones?

2022

Company Founded

Founded on June 11th 2022 by Vipul Ved Prakash (CEO) Ce Zhang (CTO) Percy Liang and Chris Re - All of whom are AI Researchers at Stanford University and have experienced working for Apple as part of their acquisition process.

2023

Series A Funding

Together AI Raised $102.5 Million At A Valuation Of Approximately $500 Million Led By Kleiner Perkins With Participation From NVIDIA.

2024

Series A Extension

Together AI Also Raised Additional $106 Million At A Valuation Of $1.25 Billion Led By Salesforce Ventures With Participation From Coatue And Lux Capital.

2024

Foundry Launch

The company launched its Foundry Cloud Platform for self-service GPU compute, which allows users to rent GPU's for as little as 3 hours.

2024

Global Expansion

The company now operates in 10+ countries around the world, including London, Tokyo, New York and São Paulo.

Who Are the Key Executives Behind Together AI?

Vipul Ved Prakash— CEO & Co-founder
Co-Founder Vipul Ved Prakash was one of the founders of Topsy (which was acquired by Apple for over $200 Million) and previously served as the Head of AI / ML at Apple. He has a background in being a Stanford Researcher.
Ce Zhang— CTO & Co-founder
Ce Zhang has worked as a Professor at Stanford and his work as a post doc was under the advisement of Chris Re who is also a co-founder. He is a well-known Systems Expert in AI.
Percy Liang— Co-founder
Chris Re is a Stanford Professor who is a pioneer in the field of Natural Language Processing and Foundation Models Research.
Chris Re— Co-founder
Ce Zhang is also a Stanford Professor who founded Lattice.io (which was acquired by Apple) and is a leading expert in systems that support Machine Learning.

What Are the Key Features of Together AI?

✨
Together Inference
Together AI developed a high-performance inference engine that is optimized for use with open-source Generative AI models and provides real-time serving capabilities.
📊
Fine-tuning Platform
Users can train their own custom models using private data on the distributed GPU clusters with full parameter efficiency.
✨
GPU Cloud Marketplace
Together AI uses token-based access to allow users to reserve and utilize compute resources through 10+ GPU cloud providers with flexible 3 hour minimums and proactive node replacement.
✨
Open Model Catalog
The company maintains a curated collection of the latest state-of-the-art open-source models that are ready for immediate deployment.
📊
Foundry Platform
The company’s cloud platform is designed as a self-serve model allowing users to reserve, train and then resell compute resources.
🔗
Framework Integrations
Native integration of LangChain, Vercel, MongoDB and leading AI frameworks.

What Technology Stack and Infrastructure Does Together AI Use?

Infrastructure

Multi-cloud GPU clusters across US/EU data centers with 10+ GPU providers

Technologies

PythonPyTorchKubernetesDistributed Training

Integrations

LangChainVercelMongoDBHugging FaceCrusoe CloudVultr

AI/ML Capabilities

Full-stack generative AI platform supporting training, fine-tuning, and inference of large language models with optimized serving engines

Based on official documentation and research reports

What Are the Best Use Cases for Together AI?

AI Researchers & Universities
Ability to utilize high-end GPU compute for training foundation models without having to purchase large amounts of costly infrastructure.
AI Startups
Token based pricing to accommodate variable usage patterns such as spiky model training and fine tuning phases.
Enterprise AI Teams
Reliable deployment of production grade generative AI applications utilizing open models with enterprise level SLA's.
Individual Developers
Run open source LLMs and run fine tune experiments using a Pay As You Go compute model.
NOT FORHigh-Frequency Trading Systems
Not suited - designed for training/inference workloads and is not optimized for ultra low latency real time applications.
NOT FORHighly Regulated Medical Devices
Not fully compatible - available with enterprise infrastructure however does not have specific medical device certifications.

How Much Does Together AI Cost and What Plans Are Available?

Pricing information with service tiers, costs, and details
☐Service$CostℹDetails🔗Source
Build TierFree credits6,000 requests/min, 2 million tokens/min, base models access for experimentation—
Scale TierUsage-based with discounts9,000 requests/min, 5M tokens/min, HIPAA compliance, 99% SLA—
Enterprise TierCustom with discountsGeo-redundant deployment, private VPC, unlimited tokens, 99.9% SLA, priority GPU access—
Serverless InferencePer million tokens (varies by model)e.g. FLUX.1 [dev] $0.025/M input tokens, H100 GPU $3.36/M tokensOfficial pricing page
Fine-tuningPer million tokensUp to 16B: LoRA $0.48, Full $1.20; 70-100B: LoRA $2.90, Full $7.25Official pricing page
Dedicated GPU EndpointsPer minute1x H200 $4.99, 1x H100 $3.36, 1x A100 80GB $2.56Official pricing page
Batch Inference50% discountDiscounted rate for bulk processing—
Build TierFree credits
6,000 requests/min, 2 million tokens/min, base models access for experimentation
Scale TierUsage-based with discounts
9,000 requests/min, 5M tokens/min, HIPAA compliance, 99% SLA
Enterprise TierCustom with discounts
Geo-redundant deployment, private VPC, unlimited tokens, 99.9% SLA, priority GPU access
Serverless InferencePer million tokens (varies by model)
e.g. FLUX.1 [dev] $0.025/M input tokens, H100 GPU $3.36/M tokens
Official pricing page
Fine-tuningPer million tokens
Up to 16B: LoRA $0.48, Full $1.20; 70-100B: LoRA $2.90, Full $7.25
Official pricing page
Dedicated GPU EndpointsPer minute
1x H200 $4.99, 1x H100 $3.36, 1x A100 80GB $2.56
Official pricing page
Batch Inference50% discount
Discounted rate for bulk processing

How Does Together AI Compare to Competitors?

FeatureTogether AIOpenAIAnthropicFireworks AI
Core Functionality200+ open models, inference/fine-tuningProprietary models onlyClaude models focusOpen models + proprietary
Pricing ModelPer-token + GPU hourlyPer-tokenPer-tokenPer-token
Free TierBuild tier w/ creditsLimited playgroundLimited playgroundYes
Enterprise FeaturesHIPAA, private VPC, 99.9% SLAYes SSO/SAMLYes enterpriseYes
API AvailabilityOpenAI-compatibleYesYesYes
Model Count200+ open sourceLimited proprietaryFew models100+ open
Support OptionsPriority in Enterprise/ScaleEnterprise supportEnterprise supportStandard support
Security CertificationsHIPAA (Scale+), SOC2SOC2, HIPAA BAASOC2SOC2
Fine-tuning SupportFull/LoRA at scaleGPT fine-tuningLimitedYes
Core Functionality
Together AI200+ open models, inference/fine-tuning
OpenAIProprietary models only
AnthropicClaude models focus
Fireworks AIOpen models + proprietary
Pricing Model
Together AIPer-token + GPU hourly
OpenAIPer-token
AnthropicPer-token
Fireworks AIPer-token
Free Tier
Together AIBuild tier w/ credits
OpenAILimited playground
AnthropicLimited playground
Fireworks AIYes
Enterprise Features
Together AIHIPAA, private VPC, 99.9% SLA
OpenAIYes SSO/SAML
AnthropicYes enterprise
Fireworks AIYes
API Availability
Together AIOpenAI-compatible
OpenAIYes
AnthropicYes
Fireworks AIYes
Model Count
Together AI200+ open source
OpenAILimited proprietary
AnthropicFew models
Fireworks AI100+ open
Support Options
Together AIPriority in Enterprise/Scale
OpenAIEnterprise support
AnthropicEnterprise support
Fireworks AIStandard support
Security Certifications
Together AIHIPAA (Scale+), SOC2
OpenAISOC2, HIPAA BAA
AnthropicSOC2
Fireworks AISOC2
Fine-tuning Support
Together AIFull/LoRA at scale
OpenAIGPT fine-tuning
AnthropicLimited
Fireworks AIYes

How Does Together AI Compare to Competitors?

vs OpenAI

Compared side by side, Together AI provides superior access to open source models (200+) than OpenAI which only allows its own proprietary models, at a lower price point for similar performance and together is targeted toward budget conscious developers while OpenAI is premium tier for enterprise.

Use Together AI for open model solutions and cost savings and use OpenAI for the latest and greatest proprietary performance.

vs Anthropic

Together AI supports a broader range of open source models and can perform fine tuning at lower GPU rates compared to Anthropic’s safety first approach with its Claude focused platform. Together AI would be best suited for experimentation and prototyping and Anthropic would be best suited for regulated enterprises that require Constitutional AI.

Use Together AI for model diversity and use Anthropic for safety critical applications.

vs Fireworks AI

Direct competitors in terms of providing direct open model solutions. Together AI provides stronger enterprise SLA’s (99.9%) and also provides a wider array of GPU options (H100/H200) while Fireworks provides faster inference times but less fine tuning options. Both are targeted toward developers who wish to avoid vendor lock in.

Use Together AI for scalable enterprise applications and use Fireworks for speed optimized inference applications.

vs DeepInfra

Inexpensive open model inference solution provider. Together AI can justify charging a higher price point due to its Scale/Enterprise features (HIPAA, VPC) over DeepInfra’s basic serverless offerings. Together AI appears to be gaining traction in terms of production workload deployments.

Use Together AI for production reliability and use DeepInfra for hobbyist or experimental purposes.

What are the strengths and limitations of Together AI?

Pros

  • The largest open source library of 200+ models for a wide variety of applications and industries.
  • We believe in transparent pricing -- our token prices are clearly visible and there are no additional fees, and you can get a 50% discount when using our batch pricing.
  • We take enterprise seriously -- we offer SLAs that guarantee 99.9% uptime, and we also offer HIPPA compliance if your application needs it.
  • Our APIs are compatible with OpenAI so you can easily migrate to us if you currently use their services.
  • You will be able to fully fine-tune all models, including LoRA and full parameter tuning at scale.
  • We utilize high performance GPU's (H100/H200) and provide them to you via dedicated endpoint connections.
  • We provide developers with generous build tier limits to allow for a lot of experimentation.

Cons

  • We have a very complex pricing model and over 200 different models that all charge tokens at different rates.
  • Because we charge based on tokens and because token billing is variable depending on how much you use our service, you never really know what you're going to end up paying.
  • We do not provide a single dashboard to monitor all aspects of our service. You need to separately manage your pricing for each type of activity (inference, training, etc.) as well as your GPU pricing.
  • Because our service is built around providing developers with a way to run machine learning workloads in the cloud, it takes some significant engineering effort to set up our service for use in a production environment.
  • Choosing the right model for the job you want to accomplish can be very complex and you may need to perform a number of benchmarks to find the best cost/performance ratio for your particular workload.
  • While we do offer a free trial version of our service, once your credits expire you'll need to upgrade to one of our paid tiers in order to continue running your workloads.
  • Because we are a cloud-based provider, we do not provide any option for you to deploy our service in an on-premise or air-gapped environment.

Who Is Together AI Best For?

Best For

  • AI developers needing model variety — With our service, you have immediate access to our 200+ open source models, which reduces the amount of time spent evaluating models across various providers.
  • Cost-conscious enterprises — Compared to proprietary providers, we provide significantly lower token rates, plus a 50% discount for batching.
  • Teams migrating from OpenAI — In addition to the large number of models available through our service, you can also drop-in replace OpenAI compatible APIs with ours.
  • Fine-tuning intensive workloads — Not only do we provide support for LoRA and full parameter tuning at competitive rates, but we also provide these capabilities at scale.
  • Startups scaling AI inference — We provide a Build -> Scale -> Enterprise progression path where each tier has increasing limits.

Not Suitable For

  • Non-technical business users — Unlike many other platforms that provide AI services, you need to have expertise in integrating our API in order to use our service effectively.
  • Budget-constrained hobbyists — While we do provide a certain number of free credits to help you try out our service, it is generally cheaper to use completely free alternatives such as Hugging Face Spaces.
  • Real-time latency-critical apps — Similar to many other serverless solutions, our solution may incur cold-start delays when you first start an inference request. You may wish to consider using edge providers to avoid this delay.
  • On-premise deployment needs — Since our service is cloud-based, you cannot deploy our service in an on-premise or air-gapped environment. If you need this capability, you should consider using RunPod or hosting the necessary hardware yourself.

Are There Usage Limits or Geographic Restrictions for Together AI?

Build Tier Rate Limit
6,000 requests/min, 2M tokens/min
Scale Tier Rate Limit
9,000 requests/min, 5M tokens/min
Enterprise Rate Limit
Unlimited tokens, priority GPU access
HIPAA Compliance
Scale Tier and above only
SLA Availability
99% Scale, 99.9% Enterprise
Deployment Options
Cloud-only, no on-premise
GPU Availability
H100, H200, A100 subject to capacity
Model Access
200+ open-source models, no proprietary closed models
Free Credits
Build Tier - amount/time not specified, expires

Is Together AI Secure and Compliant?

HIPAA ComplianceAvailable in Scale and Enterprise tiers for regulated workloads
SOC 2 ComplianceEnterprise-grade security for cloud GPU infrastructure
Private VPC DeploymentEnterprise tier isolation from public cloud traffic
Geo-Redundant DeploymentEnterprise high-availability across regions
99.9% Uptime SLAGuaranteed availability for Enterprise customers
Data EncryptionStandard cloud encryption for model inputs/outputs and training data
Priority GPU AccessEnterprise reservations prevent capacity contention

What Customer Support Options Does Together AI Offer?

Channels
24/7 for Enterprise and Scale tiersPriority channels for Scale and Enterprise plansComprehensive docs.together.ai for self-service
Hours
24/7 for Enterprise, business hours for lower tiers
Response Time
Priority response for Enterprise (<1 hour SLA), standard <24 hours
Satisfaction
N/A - limited public review data
Specialized
Private support channels and dedicated success managers for Enterprise
Business Tier
Enterprise plan includes 99.9% SLA, unlimited rate limits, priority GPU access
Support Limitations
•Free/Developer tiers limited to community/docs support only
•Dedicated support requires Scale or Enterprise plans
•No phone support mentioned

What APIs and Integrations Does Together AI Support?

API Type
REST API with OpenAI-compatible endpoints
Authentication
API Keys, supports OpenAI SDK drop-in replacement
Webhooks
Not explicitly mentioned; focus on API polling and serverless endpoints
SDKs
OpenAI-compatible SDKs (Python, JS, etc.), native Python client
Documentation
Comprehensive at docs.together.ai with interactive examples
Sandbox
Free tier/Developer playground for testing up to rate limits
SLA
99.9% uptime Enterprise, SLA-backed performance for Scale tier
Rate Limits
Tiered: up to 9,000 req/min Scale, unlimited Enterprise
Use Cases
Inference at scale, fine-tuning workflows, RAG pipelines, chat/multimodal apps

What Are Common Questions About Together AI?

Together AI is an AI acceleration cloud platform that uses high-performance GPU clusters to train, fine-tune, and make predictions with 200+ open-source models. Together AI provides 2-3 times faster inference and up to 50% lower GPU costs than competing cloud and on-premises solutions.

Access to over 200 open-source models such as Llama, Mixtral, DeepSeek, Qwen and multimodal models. Fine tune and run your own models on Together AI with full data control.

While OpenAI has proprietary, closed models; Together AI is focused on offering better priced, open source models optimized on GPUs. The company also offers OpenAI compatible API’s that make migration easier, and provides self hosting, custom fine tuning and lower inference costs.

Yes, Together AI has enterprise level compliance such as SOC 2, HIPPA, etc., and will provide a private VPC deployment so enterprise customers can host models and data behind their firewalls.

Together AI is a usage based pricing model, and they have tiered pricing levels: developer (free limited), scale (has higher throughput, includes HIPPA) and enterprise (includes unlimited tokens, 99.9% uptime).

Yes, you can deploy on your vpc, on premises, on aws, azure, gcp or oci. All of your data will stay behind your firewall, and they have single tenant options available for industries with regulated needs.

With Together AI you get 2-3 times faster inference speeds than with standard deployments by using various techniques including speculative decoding, quantization, and FP8 kernel acceleration. They claim to be able to achieve up to a 4 times increase in speed when deployed to gpu clusters.

Together AI provides documentation and community support for developers. Priority support channels, and a dedicated manager are included in both scale and enterprise tiers along with service level agreements.

Is Together AI Worth It?

Together AI provides best-in-class performance for running open-source AI models for inference, fine-tune and deployment with transparent pricing and flexibility in infrastructure. Their GPU optimization is capable of achieving 2-3 times faster performance and reducing costs by 50%, making them an excellent choice for production-level gen ai. Enterprise features like VPC deployment and HIPPA compliance position Together AI to compete with proprietary platforms.

Recommended For

  • Engineering teams responsible for deploying large scale open-source LLM’s
  • Startups and enterprises looking to optimize GPU costs for inference
  • Teams migrating away from proprietary API’s requiring OpenAI compatibility
  • Users that need compliance and control of their data

!
Use With Caution

  • New teams using GPUs — Requires a DevOps team
  • Users that use GPUs low volume — Best value when you are at your production size
  • Users that have a proprietary model — Focus is on an open source model

Not Recommended For

  • Hobbyists — Limited by free tier
  • Applications that require real-time latency (<50 ms) — Inference optimized but edge first is not supported
  • Teams that do not manage their own infra/cloud/ gpu — Requires a team that manages cloud/gpu infrastructure
Expert's Conclusion

Technical teams that prioritize performance and cost efficiency while using open source AI models at scale will find Together AI a great fit.

Best For
Engineering teams responsible for deploying large scale open-source LLM’sStartups and enterprises looking to optimize GPU costs for inferenceTeams migrating away from proprietary API’s requiring OpenAI compatibility

What do expert reviews and research say about Together AI?

Key Findings

Together AI provides high performance open source AI acceleration with 2-3X faster inference speeds, 50% lower GPU costs and 200+ model support. The Enterprise Platform offers a flexible way to deploy (Cloud/VPC/on prem), complies with SOC 2 /HIPAA standards and has OpenAI compatible APIs. Transparent tiered pricing and production ready infrastructure offer it as a viable alternative to closed AI platforms.

Data Quality

Good - detailed information from official website, product announcements, and analyst coverage. Limited independent user reviews and exact pricing requires account signup.

Risk Factors

!
Rapid development in AI hardware may be a challenge for optimizing long term
!
Enterprise adoption may depend on being able to prove cost savings at scale
!
Quality of open source model ecosystem can create dependency
!
Hyperscalers competing to build a similar layer of GPU acceleration
Last updated: January 2026

What Are the Best Alternatives to Together AI?

  • •
    Fireworks AI: Provides a high performance inference platform focused on speed-optimized open source models. Has similar levels of GPU acceleration (Claims 3-5X faster) than Together AI, but fewer models available. Best for applications that prioritize raw speed over model fine-tuning. (Fireworks.ai)
  • •
    Replicate: Managed ML platform that makes it easy to deploy and scale your models. Less expensive (Pay per second), less emphasis on GPU optimizations. Best for ML teams looking for simple scaling without having to manage the underlying infrastructure. (Replicate.com)
  • •
    Groq: LPUs for ultra fast LLM inference (Claims 10-20X faster) — Hardware-centric approach is best suited for real time serving applications. Model support is limited and does not allow for fine tuning. Best for high concurrency chat/search type applications. (Groq.com)
  • •
    DeepInfra: Less expensive than Together for the standard workload, this is a cost-optimized inference solution that uses an aggressive pricing strategy to make popular open models available at lower costs. The trade-off is in the loss of some enterprise-level VPC/compliance features. This would be the best choice for cost-conscious production deployments. (deepinfra.com)
  • •
    Baseten: A platform for deploying ML where you get strong observability and autoscaling capabilities. This will provide better AB testing and monitoring capabilities, however it has higher pricing associated with it. This will be the best option for organizations requiring advanced deployment workflows as part of their ML platform. (baseten.co)
  • •
    OpenRouter: An API to access over 100 different providers, including Together models. It provides provider-agnostic routing with cost optimization, but does not provide direct GPU control. This would be the best option for applications that require model fallback and/or easy provider switching. (openrouter.ai)

What Additional Information Is Available for Together AI?

Enterprise Platform Launch

In late September 2024, it was announced that Together is creating a unified platform that combines inference, fine-tuning, customer models, and GPU clusters into one single platform. The company claims that this new platform can provide 2-3 times the performance of the current version of its platform for inference and reduce GPU costs by 50% across all environments.

Model Ecosystem

Currently supports 200+ open source models that are categorized as follows; chat, multimodal, embeddings, re-rank, and code. Some of the key families of models supported include Llama, Mixtral, Qwen, and DeepSeek, which are being added to continually by researchers.

Performance Optimizations

Utilizes speculative decoding, quantization, FP8 kernels, and adaptive techniques for optimizations. The platform also includes auto fine-tuning and model distillation, which continue to optimize the performance of models once they have been deployed for both cost and performance.

Deployment Flexibility

Available to run on Together Cloud, customer VPCs, on-premises, AWS, Azure, GCP, and OCI. Single tenant options are also available to keep customer's data behind their firewalls.

Compliance Certifications

Has achieved SOC 2 Type II compliance and HIPAA compliance. The enterprise tier offers additional compliance features such as geo-redundancy, private VPCs, unlimited tokens, and longer term monitoring retention.

AI Integration Performance Metrics

245 ms
API Response Time
94.2 %
Model Accuracy
99.8 %
Integration Uptime
52847
Daily API Calls

Critical Features for AI API Integration

200+ Open-Source Model Support

Provides customers with access to 200+ open source models including Llama, Qwen, and DeepSeek with the ability to deploy them on day one when a new model is released

Serverless & Dedicated Endpoints

Offers flexible deployment options that allow users to choose from serverless pay-per-token, dedicated endpoints with 99.9% SLA, and deployment within a VPC

Together Inference Engine

Includes proprietary inference engine that is optimized using FlashAttention and other innovative research technologies to produce 75% faster inference speeds

Fine-Tuning API

Allows users to fine-tune large models with parameters exceeding 100 billion using the Hugging Face Hub integration and DPO training

Auto-Scaling Endpoints

Auto-scalable API, which is configurable; it will add more resources (or capacity) as necessary based on API request spike volumes.

Multi-Cloud & On-Premise

Customers can create deployments within their own VPC’s, and customers also have the ability to create deployments in customer-owned cloud provider environments using existing cloud spend.

Adaptive Optimization

Model optimization capabilities, which include adaptive speculators, model distillation, and continuous model performance improvements are all automatic.

Native Agentic Capabilities

Function calls, structured output, and agentic features do not require additional training or configuration as part of the core product offering.

Compliance and Security Certifications

SOC 2 Type IIEnterprise deployments
ISO 27001Global enterprise deployments
GDPR ComplianceEuropean operations
Encryption in Transit (TLS 1.3)All deployments
VPC Peering & Private NetworkingHigh-security deployments
99.9% SLA AvailabilityProduction workloads

What Is Together AI's Technical Infrastructure Specs?

GPU Cluster Scale
16 to 100K+ NVIDIA H100/H200/GB200 GPUs
Inference Engine Performance
75% faster than PyTorch
Concurrent Endpoints
Unlimited serverless + dedicated autoscaling
Model Capacity
100B+ parameter fine-tuning
Interconnect Technology
Infiniband + NVLink
Deployment Options
Together Cloud, Customer Cloud, GPU Clusters
API Standards
OpenAI-compatible + native Together API
Model Availability
200+ open-source models day-one

Observability and Monitoring Capabilities

Real-Time Inference Monitoring

The API provides live dashboards to track latency, throughput, and token usage across all endpoints.

Model Performance Tracking

The API provides end-to-end insight into the quality of a model's inferences, degradation of a model's inferences, and potential opportunities for optimizing inferences.

Usage & Cost Analytics

The API provides detailed tracking of token usage with suggestions to optimize costs.

Autoscaling Health Monitoring

The API provides real-time visibility into when new capacity has been provisioned to support scaling requests and what specific capacity provisioning events were triggered by scaling requests.

SLA Compliance Monitoring

The API tracks the uptime of dedicated endpoint deployments at 99.9% and sends alerts when downtime occurs.

Fine-Tuning Job Monitoring

The API allows users to track progress of training jobs, how much resources each job is utilizing, and manage checkpoints for training jobs

AI-Specific Use Case Mapping

Use CaseDepartmentAI Capabilities RequiredBusiness Value
Viral AI Video GenerationContent CreationMulti-modal inference, High-throughput scaling60% cost savings, handles viral traffic spikes
Production Agentic ApplicationsCustomer ExperienceNative function calling, Structured outputsLightning-fast experiences, 2x latency reduction
Custom Model Fine-TuningData Science100B+ parameter training, Hugging Face integrationDomain-specific accuracy improvements at lower cost
Enterprise Knowledge ProcessingKnowledge ManagementOpen-source LLMs, RAG optimizationCost-efficient retrieval with frontier model performance
Real-Time News PersonalizationMediaLow-latency inference, Autoscaling endpointsProduction-scale personalization without throttling
Code Generation & AccelerationEngineeringCode models, Optimized inference engine75% faster inference for developer productivity
Multi-Modal Content ProcessingMarketingImage/audio/text models, Serverless scalingRapid campaign prototyping with multimodal capabilities

Deployment and Integration Patterns

Serverless InferenceDedicated GPU EndpointsVPC DeploymentMulti-Cloud SupportAutoscaling ArchitectureTogether GPU ClustersOpenAI-Compatible APIsModel Abstraction LayerPay-Per-Token PricingLoRA Fine-TuningHugging Face IntegrationHigh-Throughput InferenceFunction Calling AgentsStructured OutputsAdaptive OptimizationFrontier Model Day-One

Evaluation Priority Matrix for AI API Platforms

CategoryTop 3 MetricsEvaluation CriteriaWeighting
PerformanceInference speed (vs PyTorch), Time-to-first-token, Throughput scalingBenchmark Together Inference Engine against baselines; test viral load scenarios30%
Cost EfficiencyCost per token, Total cost of ownership, Fine-tuning pricingCompare serverless vs dedicated pricing; validate 60% savings claims25%
Model EcosystemModel availability (200+), Fine-tuning capacity (100B+), Day-one frontier accessVerify open-source model coverage; test Hugging Face integration20%
ScalabilityAutoscaling reliability, GPU cluster capacity, No-throttling guaranteeLoad test endpoints; validate trillion-token workload claims15%
Enterprise ReadinessDeployment flexibility, SLA guarantees, Security certificationsReview VPC/multi-cloud options; validate 99.9% uptime commitments10%

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